Apparatus and method for determining kinetic information

ABSTRACT

A method of determining kinetic information may include: receiving a plurality of raw information related to a plurality of objects using a radar device provided in a vehicle; obtaining, by analyzing the plurality of raw information, a plurality of candidate kinetic information related to the vehicle; estimating, through spatial filtering, current first kinetic information related to the vehicle from the plurality of candidate kinetic information; and correcting, using a kinetic model, the estimated current first kinetic information based on current first kinetic information, wherein the current first kinetic information is predicted from previous first kinetic information related to the vehicle using a kinetic model.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of application No. 16/854,423, filedon Apr. 21, 2020, which claims priority under 35 U.S.C. § 119 to KoreanPatent Application No. 10-2019-0150691 filed on Nov. 21, 2019, in theKorean Intellectual Property Office, the entire disclosure of which areincorporated herein by reference for all purposes.

BACKGROUND 1. Field

The following description relates to technology for determining kineticinformation related to a vehicle, for example, technology fordetermining kinetic information related to a vehicle using a radardevice.

2. Description of Related Art

Kinetic information related to a vehicle may be estimated using a singleradar device. Estimation using a single radar device assumes asingle-track model with the Ackerman condition and is used to estimate alongitudinal speed and a yaw rate of the vehicle. To estimate thekinetic information, target detection is performed based on data sensedby the radar device. The kinetic information may be estimated based onangle and speed information related to detected targets.

SUMMARY

This Summary is provided to introduce a selection of concepts in asimplified form that are further described below in the DetailedDescription. This Summary is not intended to identify key features oressential features of the claimed subject matter, nor is it intended tobe used as an aid in determining the scope of the claimed subjectmatter.

In one general aspect, a method of determining kinetic informationincludes: receiving a plurality of raw information related to aplurality of objects using a radar device provided in a vehicle;obtaining, by analyzing the plurality of raw information, a plurality ofcandidate kinetic information related to the vehicle; estimating,through spatial filtering, current first kinetic information related tothe vehicle from the plurality of candidate kinetic information; andcorrecting, using a kinetic model, the estimated current first kineticinformation based on current first kinetic information, wherein thecurrent first kinetic information is predicted from previous firstkinetic information related to the vehicle using a kinetic model.

The correcting of the estimated current first kinetic information mayinclude: predicting the current first kinetic information based on theprevious first kinetic information using the kinetic model; andcorrecting the estimated current first kinetic information based on thepredicted current first kinetic information.

The correcting of the estimated current first kinetic information mayfurther include obtaining corrected current first kinetic information byapplying a weighted mean to the estimated current first kineticinformation and the predicted current first kinetic information.

The obtaining of the corrected current first kinetic information mayinclude: calculating a reliability of the estimated current firstkinetic information and a reliability of the predicted current firstkinetic information; and obtaining the corrected current first kineticinformation by applying the weighted mean to the estimated current firstkinetic information and the predicted current first kinetic informationbased on the reliability of the estimated current first kineticinformation and the reliability of the predicted current first kineticinformation.

The correcting of the estimated current first kinetic information mayinclude correcting the estimated current first kinetic information basedon the predicted current first kinetic information using a Kalmanfilter.

The method may further include: obtaining second kinetic informationrelated to the vehicle by analyzing sensor information received througha sensor. The correcting of the estimated current first kineticinformation may include correcting the estimated current first kineticinformation based on the second kinetic information and the predictedcurrent first kinetic information.

The method may further include: generating image information of a spacein which the vehicle is positioned, based on the previous first kineticinformation and previous position information related to the vehicle;and estimating current position information related to the vehicle basedon the image information and the plurality of raw information. Thecorrecting of the estimated current first kinetic information mayinclude correcting the estimated current first kinetic information andthe estimated current position information based on the predictedcurrent first kinetic information and the estimated current positioninformation.

The generating of the image information may include generating the imageinformation using simultaneous localization and mapping (SLAM).

The spatial filtering may include a process of selecting candidatekinetic information related to a stationary object from among theplurality of candidate kinetic information in a same time frame.

In another general aspect, a non-transitory computer-readable storagemedium stores instructions that, when executed by a processor, cause theprocessor to perform the method described above.

In another general aspect, an apparatus for determining kineticinformation includes a radar device and a processor. The radar device isconfigured to receive a plurality of raw information related to aplurality of objects. The processor is configured to: obtain, byanalyzing the plurality of raw information, a plurality of candidatekinetic information related to a vehicle; estimate, through spatialfiltering, current first kinetic information related to the vehicle fromthe plurality of candidate kinetic information; and correct theestimated current first kinetic information based on current firstkinetic information, wherein the current first kinetic information ispredicted from previous first kinetic information related to the vehicleusing a kinetic model.

The processor may be further configured to: predict the current firstkinetic information based on the previous first kinetic informationusing the kinetic model; and correct the estimated current first kineticinformation based on the predicted current first kinetic information.

The processor may be further configured to obtain corrected currentfirst kinetic information by applying a weighted mean to the estimatedcurrent first kinetic information and the predicted current firstkinetic information.

The processor may be further configured to: calculate a reliability ofthe estimated current first kinetic information and a reliability of thepredicted current first kinetic information; and obtain the correctedcurrent first kinetic information by applying the weighted mean to theestimated current first kinetic information and the predicted currentfirst kinetic information based on the reliability of the estimatedcurrent first kinetic information and the reliability of the predictedcurrent first kinetic information.

The processor may be further configured to correct the estimated currentfirst kinetic information based on the predicted current first kineticinformation using a Kalman filter.

The processor may be further configured to: obtain second kineticinformation related to the vehicle by analyzing sensor informationreceived through a sensor, and correct the estimated current firstkinetic information based on the second kinetic information and thepredicted current first kinetic information.

The processor may be further configured to: generate image informationof a space in which the vehicle is positioned, based on the previousfirst kinetic information and previous position information related tothe vehicle; estimate current position information related to thevehicle, based on the image information and the plurality of rawinformation; and correct the estimated current first kinetic informationand the estimated current position information, based on the predictedcurrent first kinetic information and the estimated current positioninformation.

The processor may be further configured to generate the imageinformation using simultaneous localization and mapping (SLAM).

The spatial filtering may include a process of selecting candidatekinetic information related to a stationary object from among theplurality of candidate kinetic information in a same time frame.

Other features and aspects will be apparent from the following detaileddescription, the drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example of a situation in which kineticinformation is determined by a kinetic information determiningapparatus.

FIG. 2 illustrates an example of a kinetic information determiningmethod.

FIG. 3 illustrates an example of determining kinetic information by akinetic information determining apparatus.

FIG. 4 illustrates an example of searching for a plurality of objects bya kinetic information determining apparatus.

FIG. 5 illustrates an example of determining kinetic information by akinetic information determining apparatus.

FIG. 6 illustrates an example of a configuration of a kineticinformation determining apparatus.

Throughout the drawings and the detailed description, the same drawingreference numerals refer to the same elements, features, and structures.The drawings may not be to scale, and the relative size, proportions,and depiction of elements in the drawings may be exaggerated forclarity, illustration, and convenience.

DETAILED DESCRIPTION

The following detailed description is provided to assist the reader ingaining a comprehensive understanding of the methods, apparatuses,and/or systems described herein. However, various changes,modifications, and equivalents of the methods, apparatuses, and/orsystems described herein will be apparent after an understanding of thedisclosure of this application. For example, the sequences of operationsdescribed herein are merely examples, and are not limited to those setforth herein, but may be changed as will be apparent after anunderstanding of the disclosure of this application, with the exceptionof operations necessarily occurring in a certain order. Also,descriptions of features that are known after an understanding of thedisclosure of this application may be omitted for increased clarity andconciseness.

The features described herein may be embodied in different forms and arenot to be construed as being limited to the examples described herein.Rather, the examples described herein have been provided merely toillustrate some of the many possible ways of implementing the methods,apparatuses, and/or systems described herein that will be apparent afteran understanding of the disclosure of this application.

Herein, it is noted that use of the term “may” with respect to anexample or embodiment, e.g., as to what an example or embodiment mayinclude or implement, means that at least one example or embodimentexists in which such a feature is included or implemented while allexamples and embodiments are not limited thereto.

Throughout the specification, when an element, such as a layer, region,or substrate, is described as being “on,” “connected to,” or “coupledto” another element, it may be directly “on,” “connected to,” or“coupled to” the other element, or there may be one or more otherelements intervening therebetween. In contrast, when an element isdescribed as “directly on,” “directly connected to,” or “directlycoupled to” another element, there can be no other elements interveningtherebetween.

As used herein, the term “and/or” includes any one and any combinationof any two or more of the associated listed items.

Although terms such as “first,” “second,” and “third” may be used hereinto describe various members, components, regions, layers, or sections,these members, components, regions, layers, or sections are not to belimited by these terms. Rather, these terms are only used to distinguishone member, component, region, layer, or section from another member,component, region, layer, or section. Thus, a first member, component,region, layer, or section referred to in examples described herein mayalso be referred to as a second member, component, region, layer, orsection without departing from the teachings of the examples.

The terminology used herein is for describing various examples only andis not to be used to limit the disclosure. The articles “a,” “an,” and“the” are intended to include the plural forms as well, unless thecontext clearly indicates otherwise. The terms “comprises,” “includes,”and “has” specify the presence of stated features, numbers, operations,members, elements, and/or combinations thereof, but do not preclude thepresence or addition of one or more other features, numbers, operations,members, elements, and/or combinations thereof.

The features of the examples described herein may be combined in variousways as will be apparent after an understanding of the disclosure ofthis application. Further, although the examples described herein have avariety of configurations, other configurations are possible as will beapparent after an understanding of the disclosure of this application.

FIG. 1 illustrates an example of a situation in which kineticinformation is determined by a kinetic information determiningapparatus.

Referring to FIG. 1 , a kinetic information determining apparatusdetermines kinetic information related to a vehicle 100 of a user. Thekinetic information determining apparatus may determine the kineticinformation related to the vehicle 100 using a radar device. The vehicle100 may be a vehicle in which the kinetic information determiningapparatus is disposed. The kinetic information determining apparatus mayincrease the accuracy of the kinetic information by performing filteringwith respect to candidate kinetic information analyzed by the radardevice. The kinetic information determining apparatus may increase theaccuracy of the kinetic information by performing spatial filtering andtemporal filtering with respect to the candidate kinetic informationanalyzed by the radar device.

The kinetic information determining apparatus may operate in a vehicleradar device, or operate as a separate signal processor that receivesdata from one or more radar devices. The separate signal processor maybe provided in a vehicle or implemented as a separate server or computerthat uses wireless communication. The kinetic information determiningapparatus may use a vehicle radar device, and may derive kineticinformation with a relatively high accuracy when using multiple radardevices at the same time.

The kinetic information determining apparatus receives a plurality ofraw information related to a plurality of objects using the radar deviceprovided in the vehicle 100. The plurality of objects may include allobjects existing outside of the vehicle 100 and reflecting singlesradiated from the radar device. The objects may include, for example,objects 104, 105, and 106 that are stationary at absolute coordinatesand moving objects 101, 102, and 103. Candidate kinetic informationanalyzed from a signal reflected from a stationary object may have ahigher reliability than candidate kinetic information analyzed from asignal reflected from a moving object.

The kinetic information determining apparatus may estimate current firstkinetic information related to the vehicle from a plurality of candidatekinetic information through spatial filtering. The kinetic informationdetermining apparatus may distinguish the signal reflected from thestationary object and the signal reflected from the moving objectthrough spatial filtering, and extract the signal reflected from thestationary object. Here, the spatial filtering may includedistinguishing a stationary object and a moving object in the same timeframe and extracting information related to the stationary object. Forexample, the kinetic information determining apparatus distinguishes thesignal reflected from the stationary object and the signal reflectedfrom the moving object in the same time frame through spatial filtering,and extracts the signal reflected from the stationary object or selectscandidate kinetic information related to the stationary object fromamong the plurality of candidate kinetic information. The kineticinformation determining apparatus may estimate current first kineticinformation from the signal reflected from the stationary object.Hereinafter, kinetic information derived through spatial filtering willbe referred to as estimated current first kinetic information. Thecurrent first kinetic information may be different from previous firstkinetic information. The kinetic information is determined through aplurality of operations. Kinetic information of a previous operationwill be referred to as previous first kinetic information, and kineticinformation of a current operation will be referred to as current firstkinetic information.

The kinetic information determining apparatus may detect objects basedon data sensed by the radar device provided in the vehicle 100. Thekinetic information determining apparatus may filter stationary objectsbased on angle and speed information related to the detected objects andestimate kinetic information based on data of the selected objects. Forexample, the kinetic information determining apparatus classifies thestationary objects using a random sample consensus (RANSAC) techniqueand estimates the kinetic information.

If most of the objects outside of the vehicle 100 are in a dynamicstate, outliers may remain despite spatial filtering. If a stationaryobject is detected, a relatively accurate longitudinal speed isestimated, but the accuracy of a yaw rate is relatively low. In general,the longitudinal speed is estimated with an accuracy of a level measuredin practice using a wheel sensor. However, the yaw rate is estimated ataccuracy with a greater error than an inertial measurement unit (IMU)commonly mounted on a vehicle. Thus, such factors may cause errors inkinetic information estimated through spatial filtering.

The kinetic information determining apparatus corrects the kineticinformation estimated by performing temporal filtering. Through thetemporal filtering, the kinetic information determining apparatus mayremove outliers not excluded through spatial filtering. The kineticinformation determining apparatus may determine more accurate kineticinformation by additionally applying temporal filtering. A value of theestimated kinetic information is used as an input of an estimatecorrector. This input is used as a measurement of the noisy kineticinformation. The estimate corrector predicts kinetic information of acurrent time based on a physical model. Internal parameters of theestimate corrector and an estimate of the kinetic information areupdated based on a value estimated by a kinetic information estimatorand a value predicted by a physical model-based predictor, and a finalestimate of the kinetic information is output.

The kinetic information determining apparatus may perform temporalfiltering to supplement inaccurate data of a sensor such as a globalpositioning system (GPS) or IMU. For example, the kinetic informationdetermining apparatus increases the accuracy of data using the Kalmanfilter. The kinetic information determining apparatus predicts amovement of the vehicle through a kinetic model. The kinetic informationdetermining apparatus uses the kinetic information estimated using theKalman filter as a noisy measurement and corrects the predicted kineticinformation through the kinetic model. The kinetic informationdetermining apparatus may determine more accurate kinetic information byrefining a noisy estimate included in each operation using the Kalmanfilter.

The kinetic information determining apparatus corrects the estimatedcurrent first kinetic information based on the current first kineticinformation predicted from the previous first kinetic informationrelated to the vehicle using the kinetic model. The kinetic informationdetermining apparatus increases the accuracy of the kinetic informationthrough temporal filtering. The kinetic information determiningapparatus predicts the current first kinetic information using thekinetic model from the previous first kinetic information. Hereinafter,the current first kinetic information derived through the kinetic modelwill be referred to as predicted current first kinetic information. Thekinetic model may include various types of physical models.

The physical models may include Constant Turn Rate and Velocity (CTRV),Constant Turn Rate and Acceleration (CTRA), Constant Curvature andAcceleration (CCA), and the like. For example, in a case of the CTRA, astate is defined as

$\begin{bmatrix}x \\y \\\psi \\v \\\overset{.}{\psi} \\a\end{bmatrix}.$

x and y denote a coordinate system that is based on a radar device or aplatform on which a radar device is mounted. ψ and {dot over (ψ)}respectively denote a heading direction and a yaw rate. v and arespectively denote a longitudinal speed and a longitudinalacceleration. Values estimated by the kinetic information determiningapparatus are v and {dot over (ψ)}. If other sensor information isretrieved, a and {dot over (ψ)} are measured in a case of the IMU, x andy are measured in a case of the GPS, and v is measured in a case of thewheel sensor. As described above, more measurements and more variousstate information are measured through additional sensors. The kineticinformation determining apparatus may determine more accurate and stablekinetic information by reflecting additional sensor information.

The kinetic information determining apparatus may derive more accurateresults by integrating the predicted current first kinetic informationwith the estimated current first kinetic information. The kineticinformation determining apparatus determines accurate kineticinformation by correcting the kinetic information from data obtained bysensing external objects by the radar device. As described above, thekinetic information determining apparatus updates parametersconstituting the kinetic information using the predicted value and theestimated value. Through this, the kinetic information determiningapparatus determines more accurate and high-reliability kineticinformation. The kinetic information determining apparatus may performtemporal filtering using results of a previous operation while at thesame time applying spatial filtering to a plurality of object candidategroups for each operation, thereby producing results more robust againstoutliers in an actual driving situation.

FIG. 2 illustrates an example of a kinetic information determiningmethod.

Referring to FIG. 2 , in operation 201, a kinetic informationdetermining apparatus receives a plurality of raw information related toa plurality of objects using a radar device provided in a vehicle. Theradar device provided in the vehicle is generally provided parallel withthe ground surface, rather than facing the air. A signal radiated fromthe radar device is reflected from the ground surface, and a receivedsignal is transmitted to the radar device.

In operation 203, the kinetic information determining apparatus obtainsa plurality of candidate kinetic information related to the vehicle byanalyzing the plurality of raw information. The kinetic informationdetermining apparatus obtains data of distance-horizontal angle-radialvelocity from received raw signals. The kinetic information determiningapparatus extracts a candidate group to be used to estimate kineticinformation in a distance-horizontality domain. The kinetic informationdetermining apparatus selects points corresponding to horizontal anglesat which the intensities of the received signals are strong as thecandidate group with respect to all regions within a detectabledistance. The candidate group includes objects in a stationary state,for example, small trees, curbs, or the ground surface. In a case ofeach object included in the candidate group, a received signal has aweak intensity and includes noise, and thus a measurement may beinaccurate. However, candidate groups corresponding to the number ofcells with respect to the total distance axis of the radar device may beobtained, and thus accurate information may be estimated bystatistically processing values measured from multiple candidate groups.

In operation 205, the kinetic information determining apparatusestimates current first kinetic information related to the vehicle fromthe plurality of candidate kinetic information through spatialfiltering. The kinetic information determining apparatus filtersdiscordant data between kinetic information extracted from multipleobjects using a RANSAC technique. Outliers have kinetic information indiscord with each other and thus, are excluded through the RANSACtechnique. The kinetic information determining apparatus removes anoutlier of a current operation using the RANSAC technique. However, theRANSAC technique is merely provided as an example. The kineticinformation determining apparatus may perform spatial filtering andkinetic information estimation through various types of outlier removaltechniques or other techniques using statistical models.

In operation 207, the kinetic information determining apparatus correctsthe estimated current first kinetic information based on current firstkinetic information predicted from previous first kinetic informationrelated to the vehicle using a kinetic model. For example, the kineticinformation determining apparatus predicts the current first kineticinformation based on the previous first kinetic information using thekinetic model. The corrected current first kinetic information may beobtained by applying a weighted mean to the estimated current firstkinetic information and the predicted current first kinetic information.

The kinetic information determining apparatus may calculate areliability of the estimated current first kinetic information and areliability of the predicted current first kinetic information. Forexample, the kinetic information determining apparatus obtains thecorrected current first kinetic information by applying the weightedmean to the estimated current first kinetic information and thepredicted current first kinetic information based on the reliabilities.For example, the kinetic information determining apparatus corrects theestimated current first kinetic information based on the predictedcurrent first kinetic information using the Kalman filter.

In correcting the estimated current first kinetic information, thekinetic information determining apparatus obtains second kineticinformation related to the vehicle by analyzing sensor informationreceived through a sensor. The kinetic information determining apparatusutilizes the radar device and other sensors in estimating the kineticinformation. The kinetic information determining apparatus may obtaininformation related to sensors provided in the vehicle, for example, awheel sensor, an IMU, and/or a GPS provided in the vehicle, throughin-vehicle communication such as CAN interface. The kinetic informationdetermining apparatus corrects the estimated current first kineticinformation based on the second kinetic information and the predictedcurrent first kinetic information.

FIG. 3 illustrates an example of determining kinetic information by akinetic information determining apparatus 300.

Referring to FIG. 3 , the kinetic information determining apparatus 300includes, for example, a radar device 310, a raw information signalprocessor 320, a kinetic information estimator 330, and a kineticinformation corrector 340. However, this configuration is merelyprovided as an example. The kinetic information determining apparatus300 may, in a more general example, include a processor and a radardevice as shown in FIG. 6 .

The radar device 310 radiates a transmission signal to an outside of avehicle and receives a reception signal. The radar device 310 may obtainraw data related to an object with a strong intensity of a reflectedsignal and raw data related to an object with a weak intensity of areflected signal. In general, the intensity of a reflected signal isstrong for a moving object, and the intensity of a reflected signal isweak for an object in a stationary state.

The raw information signal processor 320 analyzes the raw informationreceived from the radar device 310. In analyzing the received rawinformation, the raw information signal processor 320 transforms adomain of the raw information. For example, the raw information signalprocessor 320 performs a distance transformation 321, a speedtransformation 323, or an angle transformation 325 with respect to theraw information. Through these transformations, the raw informationsignal processor 320 obtains candidate kinetic information.

The kinetic information estimator 330 estimates first kineticinformation from the candidate kinetic information. For example, inoperation 331, the kinetic information estimator 330 extracts acandidate group from the candidate kinetic information. In operation333, the kinetic information estimator 330 estimates the first kineticinformation from the candidate group.

The kinetic information corrector 340 corrects the estimated firstkinetic information. For example, in operation 341, the kineticinformation corrector 340 predicts kinetic information of a currentoperation from kinetic information determined in a previous operationusing a kinetic model. In operation 343, the kinetic informationcorrector 340 corrects the estimated first kinetic information based onpredicted values. Consequently, the kinetic information corrector 340outputs corrected current first kinetic information of the currentoperation.

In another example, the kinetic information determining apparatus 300may further include a sensor 350. In such an example, the kineticinformation corrector 340 receives second kinetic information from thesensor 350 and more precisely corrects the first kinetic informationbased on the second kinetic information and the predicted values. Forexample, the kinetic information determining apparatus 300 performs moreprecise position and pose estimation by integrating information relatedto heterogeneous sensors such as a camera, an ultra, and a radar sensoror GPS, IMU, and V2X communications.

FIG. 4 illustrates an example of searching for a plurality of objects bya kinetic information determining apparatus.

A kinetic information determining apparatus detects a stationary object,rather than detecting an object referred to as a “target” in terms ofgeneral radar signal processing. Objects with a strong power of areceived signal with respect to a signal transmitted by a radar deviceare typically in a dynamic state, and the target in terms of generalradar signal processing refers to a dynamic object. The dynamic objectis in a moving state, and thus if kinetic information is estimated fromraw data obtained from the dynamic object, serious errors may occur.

In FIG. 4 , objects 401, 402, 403, 404, 405, and 406 are stationaryobjects, and objects 407, 408, and 409 are dynamic objects, for example.The kinetic information determining apparatus may extract a candidategroup for estimating kinetic information from raw kinetic information.The kinetic information determining apparatus may extract the stationaryobjects 401, 402, 403, 404, 405, and 406 as the candidate group. Asdescribed above, the kinetic information determining apparatus estimateskinetic information through the kinetic information related to thestationary objects, thereby deriving more accurate kinetic information.

FIG. 5 illustrates an example of determining kinetic information by akinetic information determining apparatus 500.

A kinetic information determining apparatus may generate imageinformation of a space in which a vehicle is positioned, based onprevious first kinetic information and previous position informationrelated to the vehicle. The kinetic information determining apparatusmay estimate current position information related to the vehicle basedon the image information and a plurality of raw information. The kineticinformation determining apparatus may correct estimated current firstkinetic information and estimated current position information based onthe predicted current first kinetic information and the estimatedcurrent position information. For example, the kinetic informationdetermining apparatus generates the image information using simultaneouslocalization and mapping (SLAM).

To generate the image information, the kinetic information determiningapparatus 500 includes a radar device 510, a raw information signalprocessor 520, a kinetic information estimator 530, a kineticinformation corrector 540, and a radar imaging performer 511, forexample. However, this configuration is merely provided as an example.In a more general example, the kinetic information determining apparatus500 may include a processor and a radar device as shown in FIG. 6 .

The radar device 510 may radiate a transmission signal to an outside ofthe vehicle and receive a reception signal. The radar device 510 mayobtain raw data related to an object with a strong intensity of areflected signal and raw data related to an object with a weak intensityof a reflected signal.

The raw information signal processor 520 analyzes the raw informationreceived from the radar device 510. The raw information signal processor520 transforms a domain of the raw information. For example, in thetransforming of the domain, the raw information signal processor 520performs a distance transformation 521, a speed transformation 523, oran angle transformation 525 with respect to the raw information. Throughthese transformations, the raw information signal processor 520 obtainscandidate kinetic information.

The kinetic information estimator 530 estimates first kineticinformation from the candidate kinetic information. For example, inoperation 531, the kinetic information estimator 530 extracts acandidate group from the candidate kinetic information. In operation533, the kinetic information estimator 530 estimates the first kineticinformation from the candidate group.

The kinetic information corrector 540 corrects the estimated firstkinetic information. For example, in operation 541, the kineticinformation corrector 540 predicts kinetic information of a currentoperation from kinetic information determined in a previous operationusing a kinetic model. In operation 543, the kinetic informationcorrector 540 more accurately corrects the first kinetic informationbased on predicted values and current position information received froma scan matching performer 513. In operation 543, the kinetic informationcorrector 540 corrects the current position information received fromthe scan matching performer 513 based on the predicted values and theestimated first kinetic information.

The radar imaging performer 511 generates image information of a spacearound the vehicle based on the corrected current position information,the corrected current first kinetic information, and the candidatekinetic information. Such image information is transmitted to the scanmatching performer 513 again and used to determine kinetic informationof a subsequent operation.

For example, the kinetic information determining apparatus 500 may usethe determined kinetic information for radar imaging. For example, thekinetic information determining apparatus 500 estimates v and {dot over(ψ)} and performs radar imaging using v and {dot over (ψ)}. The kineticinformation determining apparatus 500 may estimate position informationx and y by applying scan matching to the generated radar image. Thekinetic information determining apparatus 500 may accurately correct theestimated x, y, v, and {dot over (ψ)} and apply radar imaging theretoagain.

FIG. 6 illustrates an example of a configuration of a kineticinformation determining apparatus 600.

Referring to FIG. 6 , a kinetic information determining apparatus 600includes at least one processor 601 and the radar device 310, forexample. The radar device 310 may receive a plurality of raw informationrelated to a plurality of objects.

The processor 601 may obtain a plurality of candidate kineticinformation related to a vehicle by analyzing the plurality of rawinformation. For example, the processor 601 estimates current firstkinetic information related to the vehicle from the plurality ofcandidate kinetic information through spatial filtering. The processor601 may correct the estimated current first kinetic information based oncurrent first kinetic information predicted from previous first kineticinformation related to the vehicle using a kinetic model.

The processor 601 may predict the current first kinetic informationbased on the previous first kinetic information using the kinetic model.The processor 601 may obtain the corrected current first kineticinformation by applying a weighted mean to the estimated current firstkinetic information and the predicted current first kinetic information.The processor 601 may calculate a reliability of the estimated currentfirst kinetic information and a reliability of the predicted currentfirst kinetic information, and obtain the corrected current firstkinetic information by applying the weighted mean to the estimatedcurrent first kinetic information and the predicted current firstkinetic information based on the reliabilities. For example, theprocessor 601 corrects the estimated current first kinetic informationbased on the predicted current first kinetic information using theKalman filter. The processor 601 may correct the estimated current firstkinetic information based on the predicted current first kineticinformation.

In another example, the processor 601 may obtain second kineticinformation related to the vehicle by analyzing sensor informationreceived through a sensor. The processor 601 may correct the estimatedcurrent first kinetic information based on the second kineticinformation and the predicted current first kinetic information.

In another example, the processor 601 may generate image information ofa space in which the vehicle is positioned, based on the previous firstkinetic information and previous position information related to thevehicle. The processor 601 may estimate current position informationrelated to the vehicle based on the image information and the pluralityof raw information. The processor 601 may correct the estimated currentfirst kinetic information and the estimated current position informationbased on the predicted current first kinetic information and theestimated current position information. For example, the processor 601may generate the image information using SLAM.

The radar devices 310 and 510, the raw information signal processors 320and 520, the kinetic information estimators 330 and 530, the processors,the kinetic information correctors 340 and 540, the radar imagingperformer 511, the processor 601, the processors, the memories, andother components and devices in FIGS. 1 to 6 that perform the operationsdescribed in this application are implemented by hardware componentsconfigured to perform the operations described in this application thatare performed by the hardware components. Examples of hardwarecomponents that may be used to perform the operations described in thisapplication where appropriate include controllers, sensors, generators,drivers, memories, comparators, arithmetic logic units, adders,subtractors, multipliers, dividers, integrators, and any otherelectronic components configured to perform the operations described inthis application. In other examples, one or more of the hardwarecomponents that perform the operations described in this application areimplemented by computing hardware, for example, by one or moreprocessors or computers. A processor or computer may be implemented byone or more processing elements, such as an array of logic gates, acontroller and an arithmetic logic unit, a digital signal processor, amicrocomputer, a programmable logic controller, a field-programmablegate array, a programmable logic array, a microprocessor, or any otherdevice or combination of devices that is configured to respond to andexecute instructions in a defined manner to achieve a desired result. Inone example, a processor or computer includes, or is connected to, oneor more memories storing instructions or software that are executed bythe processor or computer. Hardware components implemented by aprocessor or computer may execute instructions or software, such as anoperating system (OS) and one or more software applications that run onthe OS, to perform the operations described in this application. Thehardware components may also access, manipulate, process, create, andstore data in response to execution of the instructions or software. Forsimplicity, the singular term “processor” or “computer” may be used inthe description of the examples described in this application, but inother examples multiple processors or computers may be used, or aprocessor or computer may include multiple processing elements, ormultiple types of processing elements, or both. For example, a singlehardware component or two or more hardware components may be implementedby a single processor, or two or more processors, or a processor and acontroller. One or more hardware components may be implemented by one ormore processors, or a processor and a controller, and one or more otherhardware components may be implemented by one or more other processors,or another processor and another controller. One or more processors, ora processor and a controller, may implement a single hardware component,or two or more hardware components. A hardware component may have anyone or more of different processing configurations, examples of whichinclude a single processor, independent processors, parallel processors,single-instruction single-data (SISD) multiprocessing,single-instruction multiple-data (SIMD) multiprocessing,multiple-instruction single-data (MISD) multiprocessing, andmultiple-instruction multiple-data (MIMD) multiprocessing.

The methods illustrated in FIGS. 1 to 6 that perform the operationsdescribed in this application are performed by computing hardware, forexample, by one or more processors or computers, implemented asdescribed above executing instructions or software to perform theoperations described in this application that are performed by themethods. For example, a single operation or two or more operations maybe performed by a single processor, or two or more processors, or aprocessor and a controller. One or more operations may be performed byone or more processors, or a processor and a controller, and one or moreother operations may be performed by one or more other processors, oranother processor and another controller. One or more processors, or aprocessor and a controller, may perform a single operation, or two ormore operations.

Instructions or software to control computing hardware, for example, oneor more processors or computers, to implement the hardware componentsand perform the methods as described above may be written as computerprograms, code segments, instructions or any combination thereof, forindividually or collectively instructing or configuring the one or moreprocessors or computers to operate as a machine or special-purposecomputer to perform the operations that are performed by the hardwarecomponents and the methods as described above. In one example, theinstructions or software include machine code that is directly executedby the one or more processors or computers, such as machine codeproduced by a compiler. In another example, the instructions or softwareincludes higher-level code that is executed by the one or moreprocessors or computer using an interpreter. The instructions orsoftware may be written using any programming language based on theblock diagrams and the flow charts illustrated in the drawings and thecorresponding descriptions in the specification, which disclosealgorithms for performing the operations that are performed by thehardware components and the methods as described above.

The instructions or software to control computing hardware, for example,one or more processors or computers, to implement the hardwarecomponents and perform the methods as described above, and anyassociated data, data files, and data structures, may be recorded,stored, or fixed in or on one or more non-transitory computer-readablestorage media. Examples of a non-transitory computer-readable storagemedium include read-only memory (ROM), random-access memory (RAM), flashmemory, CD-ROMs, CD-Rs, CD+Rs, CD-RWs, CD+RWs, DVD-ROMs, DVD-Rs, DVD+Rs,DVD-RWs, DVD+RWs, DVD-RAMs, BD-ROMs, BD-Rs, BD-R LTHs, BD-REs, magnetictapes, floppy disks, magneto-optical data storage devices, optical datastorage devices, hard disks, solid-state disks, and any other devicethat is configured to store the instructions or software and anyassociated data, data files, and data structures in a non-transitorymanner and provide the instructions or software and any associated data,data files, and data structures to one or more processors or computersso that the one or more processors or computers can execute theinstructions. In one example, the instructions or software and anyassociated data, data files, and data structures are distributed overnetwork-coupled computer systems so that the instructions and softwareand any associated data, data files, and data structures are stored,accessed, and executed in a distributed fashion by the one or moreprocessors or computers.

While this disclosure includes specific examples, it will be apparentafter an understanding of the disclosure of this application thatvarious changes in form and details may be made in these exampleswithout departing from the spirit and scope of the claims and theirequivalents. The examples described herein are to be considered in adescriptive sense only, and not for purposes of limitation. Descriptionsof features or aspects in each example are to be considered as beingapplicable to similar features or aspects in other examples. Suitableresults may be achieved if the described techniques are performed in adifferent order, and/or if components in a described system,architecture, device, or circuit are combined in a different manner,and/or replaced or supplemented by other components or theirequivalents. Therefore, the scope of the disclosure is defined not bythe detailed description, but by the claims and their equivalents, andall variations within the scope of the claims and their equivalents areto be construed as being included in the disclosure.

What is claimed is:
 1. A method of determining kinetic information of amoving vehicle, the method comprising: receiving, using a radar deviceprovided in the moving vehicle, signals reflected by a plurality ofobjects; obtaining a plurality of candidate kinetic information to themoving vehicle based on the signals reflected by the plurality ofobjects; estimating, through spatial filtering, current first kineticinformation related to the moving vehicle from signals reflected by oneor more stationary objects; and predicting, using a kinetic model, thecurrent first kinetic information related to the moving vehicle based onprevious first kinetic information related to the moving vehicle; andcorrecting the estimated current first kinetic information based on thepredicted current first kinetic information.
 2. The method of claim 1,wherein the estimated first kinetic information comprises one or more ofa longitudinal speed of the moving vehicle, a longitudinal accelerationof the moving vehicle, a heading direction of the moving vehicle, and ayaw rate of the moving vehicle.
 3. The method of claim 1, wherein thecorrecting of the estimated current first kinetic information furthercomprises obtaining corrected current first kinetic information byapplying a weighted mean to the estimated current first kineticinformation and the predicted current first kinetic information.
 4. Themethod of claim 3, wherein the obtaining of the corrected current firstkinetic information comprises: calculating a reliability of theestimated current first kinetic information; calculating a reliabilityof the predicted current first kinetic information; and obtaining thecorrected current first kinetic information by applying the weightedmean to the estimated current first kinetic information and thepredicted current first kinetic information based on the reliability ofthe estimated current first kinetic information and the reliability ofthe predicted current first kinetic information.
 5. The method of claim1, wherein the correcting of the estimated current first kineticinformation comprises correcting the estimated current first kineticinformation based on the predicted current first kinetic informationusing a Kalman filter that uses the estimated current first kineticinformation as a noisy measurement.
 6. The method of claim 1, furthercomprising: obtaining second kinetic information related to the vehicleby analyzing sensor information received through one or more sensorsprovided in the vehicle, wherein the correcting of the estimated currentfirst kinetic information comprises correcting the estimated currentfirst kinetic information based on the second kinetic information andthe predicted current first kinetic information.
 7. The method of claim1, further comprising: generating image information of a space in whichthe vehicle is positioned, based on the previous first kineticinformation and previous position information related to the vehicle;and estimating current position information related to the vehicle,based on the image information and at least some of the signalsreflected by the plurality of objects, wherein the correcting of theestimated current first kinetic information comprises correcting theestimated current first kinetic information and the estimated currentposition information, based on the predicted current first kineticinformation and the estimated current position information.
 8. Themethod of claim 7, wherein the generating of the image informationcomprises generating the image information using simultaneouslocalization and mapping (SLAM).
 9. The method of claim 1, wherein thespatial filtering includes a process of selecting candidate kineticinformation obtained from signals reflected by one or more stationaryobjects from among the plurality of candidate kinetic information in asame time frame.
 10. A non-transitory computer-readable storage mediumstoring instructions that, when executed by a processor, cause theprocessor to perform the method of claim
 1. 11. An apparatus fordetermining kinetic information of a moving vehicle, the apparatuscomprising: a radar device configured to receive signals reflected by aplurality of objects; and a processor configured to: obtain a pluralityof candidate kinetic information related to the moving vehicle based onthe signals reflected by the plurality of objects; estimate, throughspatial filtering, current first kinetic information related to themoving vehicle from signals reflected by one or more stationary objectspredict, using a kinetic model, the current first kinetic informationrelated to the moving vehicle based on previous first kineticinformation related to the moving vehicle; and correct the estimatedcurrent first kinetic information based on the predicted current firstkinetic information.
 12. The apparatus of claim 11, wherein theprocessor is further configured to obtain corrected current firstkinetic information by applying a weighted mean to the estimated currentfirst kinetic information and the predicted current first kineticinformation.
 13. The apparatus of claim 12, wherein the processor isfurther configured to: calculate a reliability of the estimated currentfirst kinetic information; calculate a reliability of the predictedcurrent first kinetic information; and obtain the corrected currentfirst kinetic information by applying the weighted mean to the estimatedcurrent first kinetic information and the predicted current firstkinetic information based on the reliability of the estimated currentfirst kinetic information and the reliability of the predicted currentfirst kinetic information.
 14. The apparatus of claim 11, wherein theprocessor is further configured to correct the estimated current firstkinetic information based on the predicted current first kineticinformation using a Kalman filter that uses the estimated current firstkinetic information as a noisy measurement.
 15. The apparatus of claim11, wherein the processor is further configured to: obtain secondkinetic information related to the vehicle by analyzing sensorinformation received through one or more sensors provided in thevehicle, and correct the estimated current first kinetic informationbased on the second kinetic information and the predicted current firstkinetic information.
 16. The apparatus of claim 11, wherein theprocessor is further configured to: generate image information of aspace in which the vehicle is positioned, based on the previous firstkinetic information and previous position information related to thevehicle; estimate current position information related to the vehicle,based on the image information and at least some of the signalsreflected by the plurality of objects; and correct the estimated currentfirst kinetic information and the estimated current positioninformation, based on the predicted current first kinetic informationand the estimated current position information.
 17. The apparatus ofclaim 16, wherein the processor is further configured to generate theimage information using simultaneous localization and mapping (SLAM).18. An apparatus for determining kinetic information, the apparatuscomprising: a radar device configured to receive a plurality of rawinformation related to a plurality of objects; and a processorconfigured to: obtain, by analyzing the plurality of raw information, aplurality of candidate kinetic information related to a vehicle;estimate, through spatial filtering, current first kinetic informationrelated to the vehicle from the plurality of candidate kineticinformation; and correct the estimated current first kinetic informationbased on a predicted current first kinetic information, wherein thepredicted current first kinetic information is predicted, using akinetic model, from previous first kinetic information related to thevehicle, and wherein the spatial filtering includes a process ofselecting candidate kinetic information related to a stationary objectfrom among the plurality of candidate kinetic information in a same timeframe.
 19. The apparatus of claim 11, wherein the spatial filteringincludes a process of selecting candidate kinetic information related toa stationary object from among the plurality of candidate kineticinformation in a same time frame.